How to Successfully Accelerate the Development of Cancer Drugs
The newly established benchmark
COVID-19 vaccines were developed extremely fast, approximately nine months from pandemic emergence to Food and Drug Administration (FDA) approval in the U.S. This is, by a landslide, a record for one of the fastest development (and approval) of a vaccine, and significantly faster than traditional R&D timelines.
Pfizer’s ability to accelerate vaccine delivery may not be a surprise given the company’s years of experience and proven operational efficiencies. But it was the performance of a new, small and emerging BioPharma company, Moderna, which matched Pfizer’s development timeline to launch its very first FDA approved product in Dec 2020 – now known as the COVID-19 vaccine.
There were several pre-existing circumstances that enabled Pfizer and Moderna to expedite drug development: First, the established vaccine technology already existed (even for the new mRNA technology, there were years of prior research); and second, facing an unknown and fast-moving global pandemic, the U.S. government made the decision to invest national funds and financially de-risk these products development for the BioPharmaceutical companies.
While this unprecedented decision resulted in an ultra-rapid development timeline, it also sparked controversy and antagonism among anti-vax conspiracy theorists who equate these fast timelines with poor quality and potentially dangerous products, in short – the great R&D BioPharma achievement backfired in the marketplace.
The current (grim) status of clinical trials research
There are three study phases that take up approximately 80% of the overall drug development budget and have a direct impact on the drug development timeline. Unlike preclinical studies, clinical studies are logistically more complex, time consuming and costly. Some studies can cost $100-$150k for a single patient to participate. While study budgets remain high, it’s actually the unsustainable, slow and unpredictable patient recruitment timelines that derail most clinical trials.
Depending on the study, drug development companies typically need to enroll anywhere between 20-50 (Phase I) to hundreds (Phase II) and potentially thousands of patients (Phase III) studies. Thousands of clinical trials across the U.S. are trying to recruit new patients for potentially lifesaving treatments, yet more than 80 percent of these trials fail to meet their timelines and / or recruitment goals. These delays have an immediate, negative impact on clinical trial budgets and, worse, prolong drugs’ time to market for patients in need. Needless to say, these studies take years, cost up to hundreds of millions of dollars, and most importantly, can significantly delay regulatory approvals and much needed patients’ access to new lifesaving drugs.
The underlying issue: patients’ supply chain is limited and unpredictable
The underlying issue impacting clinical trial recruitment is an overall lack of knowledge about the availability of this treatment option and low willingness of patients to enroll in these studies.
Only approximately 2-8% percent of cancer patients actually enroll in a clinical trial, which leaves us asking the question; what happened to the other 90+ percent?
What’s more, this precious few percent is highly variable and doesn’t adequately represent the entire U.S. population and includes only around one to two percent of minority race and ethnicity populations, misrepresenting their actual population prevalence. Not only are study enrollment rates low, the barriers that prevent participation can be unpredictable depending on the patient population, their geographic location and their willingness to adhere to protocol requirements.
A closer look: the study site’s role in patients’ enrollment – monopoly?
Once the study protocol is available, the BioPharma company can begin its site selection process. Sites and investigators engagement begins with a feasibility assessment. From there, the identified sites’ investigators are given the opportunity to review the protocol to determine whether or not they can deliver and enroll the required patient population.
The underlying issue is that the feasibility data is completely subjective, and oftentimes, provides a limited or outdated view into the site’s actual level of patient numbers or access. This information can sometimes be affected by investigators confounding factors such as self-serving bias, recency bias, discounting competing studies who are to draw from the same available patients, availability heuristic, etc. In conclusion, the inaccurate estimates are compounded by multiple various mentioned biases. The result is that out of every 10 active sites, only two to three enroll any patients, while another two to three deliver no patients at all. The negative impact of these biases is so profound that the industry standard untold/hidden practice is to record only half of the promised number of patients for further feasibility assessment. For example, if the number of patients promised by the investigator is 10 over one year, the BioPharma will record five for its overall patients feasibility calculation.
This is such a prevailing practice, that it was immortalized in a sarcastic law – Lasagna’s Law (after Louis Lasagna- Tuft’s Dean of the Sackler School of Graduate Biomedical Sciences) – and is defined as:
The incidence of patient availability sharply decreases when a clinical trial begins and returns to its original level as soon as the trial is completed. Hence, overestimating potential accrual. The “law” is a common experience because investigators rely more on their impression rather than actual or experience to determine their likelihood of success in recruitment.
The BioPharma feasibility exercise is merely to satisfy its internal needs but rather to comply with Good Clinical Practice regulations as according to ICH E6 Guideline on Good Clinical Practice, the investigator should be able to demonstrate the potential for recruiting the required number of suitable patients within the agreed recruitment period (chapter 4.2.1). Therefore, it is not sufficient to just make a guess, the investigator also has to make an informed projection, i.e., based on concrete/accurate feasibility data.
Evidently, this is rarely implemented…
Addressing our industry challenge – some food for thought
It’s no secret that the current drug development process is outdated and inefficient. In fact, a term was even coined to represent the noticeable and sustained decreasing productivity over the years. Eroom’s Law, which states that the number of drugs approved per $B of R&D is halved every nine years since 1950.
This is in contrast to the semiconductor industry’s well known Moore’s Law which is the hallmark of exponentially increased productivity. The unfortunate truth is that the BioPharma industry-directed law is the inverse (and hence mirror image spelled…) of Moore’s Law. Steven Paul, M.D., the CEO, president and chairman of Karuna Therapeutics and former President of the Lilly Research Laboratories of Eli Lilly, estimated that a 50 percent development cost decrease is warranted for any new chemical entity (NCE), otherwise the industry survival is in jeopardy.
Some have maintained that industry digitization will improve productivity versus the old-fashioned paper-based approach. EDC — electronic data capture — is a good example of full digitization. 30 years ago, we used to capture all clinical study patients’ data in paper Case Report Form (CRF); later we transitioned to Fax-CRF and later Remote Data Capture (RDC), which finally evolved into currently used EDC. While it might have improved several aspects of the data workflow, it did not profoundly affect the inefficiencies of clinical studies. It was Nobel Laureate Robert Solow, who coined the Solow Paradox that IT intensive industries are not necessarily more productive.
Others have introduced new statistical methodologies to facilitate and optimize study outcomes. No drug developer will minimize the use of adaptive design claims in slides to impress upon the audience the use of the latest thinking paradigm utilization…However deeper analysis revealed a possible four percent improvement in efficiencies and while not insignificant, this is hardly revolutionary.
We need to address the tortoise in the room: slow patient enrollment is the underlying cause of inferior productivity, increased project timelines and increased costs. What’s more, is that these challenges ultimately impact the patients and society – who are faced with increasing drug costs.
In conclusion, only a very few percentage of patients participate in clinical trials with MD investigators monopolizing the ‘information’ channels’ of clinical trials to their patients. Overall, there is a significant lack of awareness that clinical studies are a viable, and many times, preferred therapeutic option compared with the standard of care, specifically in oncology.
The bottom line: can we develop cancer drugs faster?
Likely not faster than the development of COVID-19 vaccines, but it is imperative for the greater good that drugs will be developed at a faster pace. Accomplishing this goal is highly dependent on pivoting from the current, bottle-neck of patient enrollment through the monopoly of physicians investigators, and democratizing the field by opening new multiple parallel channels and most important leveraging new technologies in the process such as:
- Leveraging EMR (Electronic medical records) big data Analytics to drive sponsors to open the right number of sites at the right locations to enable sustainable and predictable flow of patients enrollment, where companies such as TriNetX are valuable
- Developing dedicated clinical trials clinics through Site Management Organizations (SMO) where patients are coming with the specific intent to engage in clinical study as a therapeutic option; with organizations such as Sarah Cannon, NEXT, START and U.S. Oncology leading in the highly competitive oncology field and organizations such as Meridian and Velocity and others in general medicine therapy fields
- Enabling the ‘Mountain to come to Mohamed’ approach, i.e., bringing the clinical study to the patients instead of schlepping the patients to the study. Companies such as Science 37, Medable, Lightship and Thread are rapidly advancing the remote and decentralized clinical trial space, such as the virtual clinical study arena that COVID-19 situation significantly pushed forward
- Easing up on patients’ participation logistics by ‘white glove’ concierge companies whose objective is to remove obstacles from the patient and family when choosing to participate in clinical studies, with travel and logistics management companies such as ClinCierge
- And last but not least fully democratizing patients’ choices thru Patient-driven enrollment with companies such as TrialJectory leveraging AI to efficiently channel the ‘Right Patient to the Right Study in a dynamic manner’
In other words, the BioPharma industry needs to learn from companies like Amazon and its scalability through leveraging efficiencies, while maintaining excellent customer service for the greater good of innovation and bringing new and necessary therapies to patients in need ASAP!